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PMSC-mediated exocrine pancreas regeneration initiates recovery of endogenous pancreas to alleviate type 1 diabetes in mice.

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Thanks for this awesome review. I’ve been trying to follow the recent papers about the unfolding stuff, and Hoel’s paper, but having read this post I feel much better about my shorthand summary: 1. Any theory that is testable will necessarily rely on measuring behavior of some system 2. Any given behavior is independent of Phi 3. Phi is not a testable value for a given theory.

Many ideas have come and gone, but Project Daedalus was a uniquely ambitious plan from the 1970s that never quite came to be.

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In the philosophy of mind, the multiple realizability thesis contends that a single mental kind (property, state, event) can be realized by many distinct physical kinds. A common example is pain. Many philosophers have asserted that a wide variety of physical properties, states, or events, sharing no features in common at that level of description, can all realize the same pain. This thesis served as a premise in the most influential argument against early theories that identified mental states with brain states (psychoneural, or mind-brain identity theories). It also served in early arguments for functionalism. Nonreductive physicalists later adopted this premise and these arguments (usually without alteration) to challenge all varieties of psychophysical reductionism. The argument was even used to challenge the functionalism it initially was offered to support. Reductionists (and other critics) quickly offered a number of responses, initially attacking either the anti-reductionist or anti-identity conclusion from the multiple realizability premise, or advocating accounts of the reduction relation that accommodated multiple realizability. More recently it has become fashionable to attack the multiple realizability premise itself. Most recently the first book-length treatment of multiple realizability and its philosophical import has appeared.

This entry proceeds mostly chronologically, to indicate the historical development of the topic. Its principle focus is on philosophy of mind and cognitive science, but it also indicates the more recent shift in emphasis to concerns in the metaphysics of science more generally. It is worth mentioning at the outset that multiple realizability has been claimed in physics (e.g., Batterman 2000), biochemistry (Tahko forthcoming) and synthetic biology (Koskinen 2019a, b). After more than fifty years of detailed philosophical discussion there still seems to be no end in sight for novel ideas about this persistent concern.

The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at the device level to enable novel compute-in-memory (CIM) operations. A key challenge in the construction of CIM architectures is the conflicting trade-off between the performance and their flexibility for various essential data operations. Here, we present a transistor-free CIM architecture that permits storage, search, and neural network operations on sub-50 nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). Our circuit designs and devices can be directly integrated on top of Silicon microprocessors in a scalable process. By leveraging the field-programmability, nonvolatility, and nonlinearity of FeDs, search operations are demonstrated with a cell footprint 0.12 μm2 when p.

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How soon will we be seeing robots walking about the street? How soon will robots join medical staff in hospitals and aid real people in life or death situations? How soon will robots replace health staff? The World Health Organization (WHO) estimates that we will see a global shortfall of 12 million health workers by 2025.

From lifting patients and delivering lab samples, to cleaning and providing companionship, care robots can help with a range of tasks across a hospital or care setting. With nurses spending up to a third of their shift on menial tasks such as collecting equipment, the expectation is that care robots will be able to take ownership of these more mundane jobs, letting health staff focus on more important tasks.

Imaging deep tissues with light is challenging. Visible light is often quickly absorbed and scattered by structures and molecules in the body, preventing researchers from seeing deeper than a millimeter within a tissue. If they do manage to probe further, substances like collagen or melanin often muddy the image, creating the equivalent of background noise through their natural fluorescence. As the authors explained, “Biological tissues have strong optical attenuation in the visible wavelength range (350–700 nm), due to the absorption of hemoglobin and melanin, as well as the tissue scattering, which fundamentally limits the imaging depth of high-resolution optical technologies.”

To wade out from these muddied waters, Yao and collaborator Vladislav Verkhusha, PhD, professor of genetics at Albert Einstein College of Medicine, developed a protein that absorbs and emits longer wavelengths of light in the near-infrared (NIR) spectrum. “Tissue is the most transparent in the 700‑1300 nm window of NIR light,” said Yao. “At those wavelengths, light can penetrate deeper into a tissue, and because there is less natural background fluorescence to filter out, we can take longer exposures and capture clearer images.”

Verkhusha and his lab used a process called directed molecular evolution to engineer their proteins, using photoreceptors normally found in bacteria as the basis for the structure. “The state-of-the-art NIR FPs were engineered from bacterial phytochrome photoreceptors (BphPs),” the team noted. “Applying rational design, we developed 17 kDa cyanobacteriochrome-based near-infrared (NIR-I) fluorescent protein, miRFP718nano.”

Short and sweet video.